Embodiments disclosed herein relate to computer software. More specifically, embodiments disclosed herein relate to computer software which identifies attributes derived from natural language processing.
Deep question answering (QA) systems answer questions by finding and evaluating candidate answers against a variety of machine learning, natural language processing, and other models generated from a corpus of relevant documents. To answer a given question, the QA system may identify a set of topics, features and attributes to evaluate using the models and content in the corpus. Questions may be posed in various forms and include reference information or notes. Concepts may be extracted from the reference information included in a question and evaluated along with the values associated with the concepts. In some cases, the question may be implicit, e.g., where the deep QA system is used to recommend a treatment for a given medical condition based on the electronic medical records of an individual. However, in some cases, there may be multiple values in the case notes for a given concept. What is needed is a technique for selecting values derived from natural language processing (NLP).